Impact of early death recording on international comparison of acute myocardial infarction mortality - administrative hospital data study using the example of Germany and the United States.

BACKGROUND: In-hospital mortality from acute myocardial infarction (AMI) is widely used in international comparisons as an indicator of health system performance. Because of the high risk of early death after AMI, international comparisons may be biased by differences in the recording of early death cases in hospital inpatient data. This study examined whether differences in the recording of early deaths affect international comparisons of AMI in-hospital mortality by using the example of Germany and the United States, and explored approaches to address this issue. METHODS: The German Diagnosis-Related Groups Statistics (DRG Statistics), the U.S. National Inpatient Sample (NIS) and the U.S. Nationwide Emergency Department Sample (NEDS) were analysed from 2014 to 2019. Cases with treatment for AMI were identified in German and U.S. inpatient data. AMI deaths occurring in the emergency department (ED) without inpatient admission were extracted from NEDS data. 30-day in-hospital mortality figures were calculated according to the OECD indicator definition (unlinked data) and modified by including ED deaths, or excluding all same-day cases. RESULTS: German age-and-sex standardized 30-day in-hospital mortality was substantially higher compared to the U.S. (in 2019, 7.3% vs. 4.6%). The ratio of German vs. U.S. mortality was 1.6. After inclusion of ED deaths in U.S. data this ratio declined to 1.4. Exclusion of same-day cases in German and U.S. data led to a similar ratio. CONCLUSIONS: While short-duration treatments due to early death are generally recorded in German inpatient data, in U.S. inpatient data those cases are partially missing. Excluding cases with short-duration treatment from the calculation of mortality indicators could be a feasible approach to account for differences in the recording of early deaths, that might be existent in other countries as well.

Health and care data: approaches to data linkage for evidence-informed policy

An indispensable prerequisite for answering research questions in health services research is the availability and accessibility of comprehensive, high quality data. It can be assumed that health services research in the comingyears will be increasingly based on data linkage, i.e., the linking, or connecting, of several data sources based on suitable common key variables. A range of approaches to data collection, storage, linkage and availability exists across countries, particularly for secondary research purposes (i.e., the use of data initially collected for other purposes), such as health systems research. The main goal of this review is to develop an overview of, and gain insights into, current approaches to linking data sources in the context of health services research, with the view to inform policy, based on existing practices in high-income countries in Europe and beyond. In doing so, another objective is to provide lessons for countries looking for possible or alternative approaches to data linkage. Thirteen country case studies of data linkage approaches were selected and analyzed. Rather than being comprehensive, this review aimed to identify varied and potentially useful case studies to showcase different approaches to data linkage worldwide. A conceptual framework was developed to guide the selection and description of case studies. Information was first identified and collected from publicly available sources and a profile was then created for each country and each case study; these profiles were forwarded to appropriate country experts for validation and completion.

[Research Data Center on Health - Vision for Further Development from the Research Perspective]. / Forschungsdatenzentrum Gesundheit ­ Vision für eine Weiterentwicklung aus Sicht der Forschung.

The German research data center for health will provide claims data of statutory health insurances. The data center was set up at the medical regulatory body BfArM pursuant to the German data transparency regulation (DaTraV). The data provided by the center will cover about 90% of the German population, supporting research on healthcare issues, including questions of care supply, demand and the (mis-)match of both. These data support the development of recommendations for evidence-based healthcare. The legal framework for the center (including §§ 303a-f of Book V of the Social Security Code and two subsequent ordinances) leaves a considerable degree of freedom when it comes to organisational and procedural aspects of the center's operation. The present paper addresses these degrees of freedom. From the point of view of researchers, ten statements show the potential of the data center and provide ideas for its further and sustainable development.

[Methodological Challenges and Lessons Learned in the Scientific Use of Data from a Private Health Insurance Company within the IPHA Project]. / Methodische Herausforderungen und Lessons Learned bei der wissenschaftlichen Nutzung von Daten eines privaten Krankenversicherungsunternehmens im Rahmen des Projekts IPHA.

The 11% of people with private health insurance (PHI) in Germany have so far been underrepresented in health services research. The scientific use of PHI data is rare. The aim of this research was to examine the scientific usability of PHI data and to highlight challenges and lessons learned in the process of data preparation and analysis using a linked dataset (n=3,109) of survey and claims data of one PHI company. Challenges were identified in the terminology of the PHI insurance, in the processing and validity of the data, and regarding insured persons without submitted billing receipts. With thorough preparation of the data and presentation of the limitations, PHI data can be used for health services research.

[Recording Trauma Care in German Hospital Discharge Data: Services Provided by Hospitals Owned by Workers' Compensation Funds and Financed through Statutory Accidental Insurance]. / Erfassung der Verletztenversorgung in Krankenhausabrechnungsdaten: Leistungen der BG Kliniken zu Lasten der Gesetzlichen Unfallversicherung.

BACKGROUND: In Germany, the Diagnosis-Related Group Statistics (DRG Statistics) represent an almost complete discharge data-based registry of inpatient services in acute care hospitals. However, services of hospitals owned by workers' compensation funds and financed through the statutory insurance for occupational accidents are excluded from the obligation of submitting hospital discharge data. Hence, the DRG statistics might be incomplete regarding inpatient services for trauma care. METHODS: In order to illustrate trauma and post-trauma care in acute care hospitals, groups of specific inpatient services were defined. Numbers of cases according to these groups were identified in the microdata of the DRG statistics, as well as in the inpatient data of all nine workers' compensation funds hospitals in Germany. By dividing cases financed through the statutory insurance for occupational accidents from cases financed through other payers, the overlap of both databases as well as the share of cases not recorded in the DRG statistics were quantified. The analysis comprised data of 2016-2018. RESULTS: Depending on the type of service, the share of cases not recorded in the DRG statistics varied between 0.1% and more than 60% (accumulated 2016 to 2018). There was under-recording of early-stage rehabilitation for traumatic brain injury (61%), treatment for traumatic paraplegia (14% for initial treatment and 23% for subsequent treatment), treatment for amputation injury (13%) and treatment for severe hand injury (5%). CONCLUSION: Regarding inpatient services that are not covered by the statutory insurance for occupational accidents, the microdata of the DRG statistics can be considered as virtually complete. However, inpatient services for trauma care are not completely recorded because discharge data are not submitted by hospitals run by workers' compensation funds when services are financed through the statutory insurance for occupational accidents. Analyses of trauma care can only be complete if data of hospitals financed by workers' compensation funds are included.

Evaluating the consequences of health policy decisions.

Uncertainty about intended and possible unintended side effects makes it important to evaluate changes following health policy decisions. A recent IJHPR article by Greenberg et al. evaluated changes in emergency department care following a directive of the Israeli Ministry of Health to limit occupancy in internal medicine wards. Over a six-year observation period, they found that one-month mortality and one-week readmissions after ED visits remained unchanged, while increases in average ED visit length, as well as increased delay time from ED admission to ward were observed. These findings help to assess the impact of the occupancy limit directive and may support future health policy decisions.However, the study by Greenberg et al. was limited by the unavailability of diagnostic data, and this illustrates a significant issue that transcends this particular study. In many countries, policy-relevant administrative data are not sufficiently available on a timely basis. Data availability is the prerequisite for monitoring developments in patterns of care following health policy changes. Besides conducting retrospective studies, timely availability of data makes it possible to establish monitoring systems which may help decision makers assess the impact of policy changes, identify undesired developments early, and recognize changes in need or demand of health services within the population.

[Definition of Variables in Hospital Discharge Data: Pitfalls and Proposed Solutions]. / Variablendefinition in fallbezogenen Krankenhausabrechnungsdaten ­ Fallstricke und Lösungsmöglichkeiten.

In Germany, the Diagnosis-Related Group Statistics (DRG Statistics) supply full coverage of inpatient episodes in acute care hospitals. The Research Data Centres of the Federal Statistical Office and the Statistical Offices of the Federal States provide the microdata of the DRG Statistics, namely hospital discharge files of each inpatient case, for scientific research. Hospital discharge data are generated for administrative purposes. As well as other data sources, they have specific features and characteristics, which should be considered in planning and designing research studies. A key challenge is the appropriate and sophisticated operationalization of units of analysis, targets variables, and other study variables. The methodological approach should consider, among other factors, differing coding behaviour between hospitals in order to minimize the risk of bias. This contribution shows by practical examples what should be incorporated in variable definition to ensure that the risk of bias by coding behaviour or other factors is minimized to the greatest possible degree. First of all, the features and characteristics of the German hospital discharge data are outlined. Based on the authors' experiences, basic steps and challenges in observational health services research studies are described. Examples are illustrated by our own calculations, derived from previous studies based on the microdata of the DRG Statistics. The reliability and validity of analyses based on hospital discharge data are crucially dependent on the appropriateness of variable definition. To minimize the risk of bias and misinterpretation, extensive preliminary considerations are required which involve clinical aspects, as well as the context of data collection and technical classification opportunities. Hopefully, there will be greater acceptance of research based on hospital discharge data, so that these valuable data will be used more frequently for research purposes in the future.

Complex gastric surgery in Germany-is centralization beneficial? Observational study using national hospital discharge data.

PURPOSE: This observational study explored the association between hospital volume and short-term outcome following gastric resections for non-bariatric indication, aiming to contribute to the discussion on centralization of complex visceral surgery in Germany. METHODS: Based on complete national hospital discharge data from 2010 to 2015, the association between hospital volume and in-hospital mortality was evaluated according to volume quintiles and volume deciles. Case-mix differences regarding surgical indication, age, sex, and comorbidities were considered for risk adjustment. In addition, rates of major complications and failure to rescue were analyzed across hospital volume categories. RESULTS: Inpatient episodes (72,528) with gastric resection were analyzed. Risk-adjusted mortality in patients treated in very low volume hospitals (median volume of 5 surgeries per year) was higher (12.0% [95% CI 11.4 to 12.5]) compared to those treated in very high volume hospitals (50 surgeries per year; 10.6% [10.0 to 11.1]). Failure to rescue patients with complications was 28.1% [27.0 to 29.3] in very low volume hospitals and 22.7% [21.6 to 23.8] in very high volume hospitals. Differences were similar within the subgroup of patients operated for gastric cancer. CONCLUSIONS: Treatment in very high volume hospitals is associated with a lower in-hospital mortality compared to treatment in very low volume hospitals. This effect seems to be determined by the ability to rescue patients who experience complications. As the observed benefit is only related to very high volumes, the results do not clearly indicate that centralization may improve short-term results substantially, unless a very high degree of centralization would be achieved. Possibly, further research focusing on other outcome measures, such as clinical processes or long-term results, might lead to divergent conclusions.

Effect of Hospital Volume on In-hospital Morbidity and Mortality Following Pancreatic Surgery in Germany.

OBJECTIVE: We aimed to determine the effect of hospital volume on in-hospital mortality, and failure to rescue following major pancreatic resections using hospital discharge data of every inpatient case in Germany. SUMMARY BACKGROUND DATA: Several studies have found strong volume-outcome relationships in pancreatic surgery, with high mortality in low-volume facilities. However, their datasets were only based on portions of national populations. In addition, these studies did not assess the effect of hospital volume according to other crucial variables such as medical indications, postoperative complications, and failure to rescue. METHODS: We studied all inpatient cases of major pancreatic surgery (n = 60,858) in Germany from 2009 to 2014, using national hospital discharge data. We evaluated the association between hospital volume and in-hospital mortality following major pancreatic resections by using multivariate regression methods. In addition, we analyzed rates of major complications and failure to rescue across hospital volume quintiles. RESULTS: Risk-adjusted in-hospital mortality varied widely across hospital volume quintiles, from 6.5% (95% CI 6.0-7.0) in very high volume hospitals to 11.5% (95% CI 10.9-12.1) in very low volume hospitals (OR 0.47, 95% CI 0.41-0.54). Rates of postoperative interventions necessary for complications and failure to rescue were lower in higher volume hospitals [eg, mortality following septic complications in very high volume hospitals: 24.2% (95% CI 22.4-26.1) vs. very low volume hospitals: 36.8% (34.9-38.7)]. Moreover, we estimated that centralization of surgical care to the minimum volume and mortality risk of the medium volume quintile could prevent at least 94 deaths per year. CONCLUSIONS: In Germany, patients who are undergoing major pancreatic resections have improved outcomes if they are admitted to higher volume hospitals. As current health policies failed to centralize pancreatic surgery procedures in Germany, new strategies to initiate a sufficient centralization process in the field of pancreatic surgery are needed.

Hospital Volume, In-Hospital Mortality, and Failure to Rescue in Esophageal Surgery.

BACKGROUND: In Germany, complex esophageal surgery is often performed in hospitals with low case numbers. For these procedures, an association exists between hospital case numbers and treatment outcomes, possibly because of differences in complication management. This aspect of the association between volume and outcome in esophageal surgery has not yet been studied in Germany. METHODS: On the basis of nationwide hospital discharge data (DRG statistics) from the years 2010 to 2015, the association between volume and outcome was analyzed in relation to in-hospital mortality, the frequency of complications, and the mortality of patients who had complications. RESULTS: 22 700 cases of complex esophageal surgery were identified. The probability of dying after esophageal surgery was much lower in hospitals with very high case numbers (median, 62 per year) than in those with very low case numbers (median, two per year), with an odds ratio (OR) of 0.50 (95% confidence interval, [0.42; 0.60]). At least one complication was documented for more than half of all patients; no association was found between the frequency of complications and the hospital case volume. The in-hospital mortality among patients who had complications was 12.3% [11.1; 13.7] in hospitals with very high case numbers and 20.0% [18.5; 21.6] in hospitals with very low case numbers. Of the 4032 procedures performed in 2015, 83% were for cancer of the esophagus. CONCLUSION: These findings indicate that the quality of care for patients undergoing esophageal surgery in Germany could be improved if more patients were treated in hospitals with high case numbers. The observed association between case numbers and outcomes is tightly linked to failure to rescue.

[Volume Growth of Inpatient Treatments for Spinal Disease - Analysis of German Nationwide Hospital Discharge Data from 2005 to 2014]. / Mengenentwicklung stationärer Behandlungen bei Erkrankungen der Wirbelsäule ­ Analyse der deutschlandweiten Krankenhausabrechnungsdaten von 2005 bis 2014.

BACKGROUND: Marked volume growth of inpatient treatments for spinal disease has been observed since diagnosis related groups (DRG) were introduced as payment for inpatient services in Germany. This study aims to analyse this increase by population and stratified by types of treatment. MATERIAL AND METHODS: Using German nationwide hospital discharge data (DRG statistics), inpatient treatments for spinal disease with or without surgery were identified. Trends in case numbers were analysed from 2005 to 2014 with consideration of demographic changes, in order to explore which age groups and which types of treatment are affected by volume growth. RESULTS: In 2014 (2005), 289 000 (177 000) inpatient treatments with surgery and 463 000 (287 000) treatments without surgery were identified. After adjusting for demographic factors, treatments with and without surgery exhibited a relative volume growth of + 50%. This increase affected higher age groups and women, in particular. Depending on the type of treatment, very different degrees of volume growth were observed. For example, disc surgeries adjusted for demographic change increased by about + 5%, whereas spinal fusion and vertebral replacement surgeries, kypho-/vertebroplasties and decompression of the spine more than doubled. Within the non-surgically treated cases, local pain therapies of the spine increased after adjustment for demographic changes by about + 142%. The conservatively treated cases showed a demographically adjusted increase of + 22%. CONCLUSION: Apart from demographic changes, this analysis cannot resolve the underlying causes of volume growth in treatments for spinal disease. However, the stratified analysis of various subgroups may help to classify these developments in a more differentiated manner. The results may support a more targeted debate about potential over- or misallocation of inpatient services in this area.

[Continuity of hospital identifiers in hospital discharge data - Analysis of the nationwide German DRG Statistics from 2005 to 2013]. / Kontinuität der Institutionskennzeichen in Krankenhausabrechnungsdaten ­ Analyse der bundesweiten DRG-Statistik von 2005 bis 2013.

BACKGROUND: In Germany, nationwide hospital discharge data (DRG statistics provided by the research data centers of the Federal Statistical Office and the Statistical Offices of the 'Länder') are increasingly used as data source for health services research. Within this data hospitals can be separated via their hospital identifier ([Institutionskennzeichen] IK). However, this hospital identifier primarily designates the invoicing unit and is not necessarily equivalent to one hospital location. Aiming to investigate direction and extent of possible bias in hospital-level analyses this study examines the continuity of the hospital identifier within a cross-sectional and longitudinal approach and compares the results to official hospital census statistics. METHODS: Within the DRG statistics from 2005 to 2013 the annual number of hospitals as classified by hospital identifiers was counted for each year of observation. The annual number of hospitals derived from DRG statistics was compared to the number of hospitals in the official census statistics 'Grunddaten der Krankenhäuser'. Subsequently, the temporal continuity of hospital identifiers in the DRG statistics was analyzed within cohorts of hospitals. RESULTS: Until 2013, the annual number of hospital identifiers in the DRG statistics fell by 175 (from 1,725 to 1,550). This decline affected only providers with small or medium case volume. The number of hospitals identified in the DRG statistics was lower than the number given in the census statistics (e.g., in 2013 1,550 IK vs. 1,668 hospitals in the census statistics). The longitudinal analyses revealed that the majority of hospital identifiers persisted in the years of observation, while one fifth of hospital identifiers changed. CONCLUSION: In cross-sectional studies of German hospital discharge data the separation of hospitals via the hospital identifier might lead to underestimating the number of hospitals and consequential overestimation of caseload per hospital. Discontinuities of hospital identifiers over time might impair the follow-up of hospital cohorts. These limitations must be taken into account in analyses of German hospital discharge data focusing on the hospital level.

Disease-Specific Trends of Comorbidity Coding and Implications for Risk Adjustment in Hospital Administrative Data.

OBJECTIVE: To investigate changes in comorbidity coding after the introduction of diagnosis related groups (DRGs) based prospective payment and whether trends differ regarding specific comorbidities. DATA SOURCES: Nationwide administrative data (DRG statistics) from German acute care hospitals from 2005 to 2012. STUDY DESIGN: Observational study to analyze trends in comorbidity coding in patients hospitalized for common primary diseases and the effects on comorbidity-related risk of in-hospital death. EXTRACTION METHODS: Comorbidity coding was operationalized by Elixhauser diagnosis groups. The analyses focused on adult patients hospitalized for the primary diseases of heart failure, stroke, and pneumonia, as well as hip fracture. PRINCIPAL FINDINGS: When focusing the total frequency of diagnosis groups per record, an increase in depth of coding was observed. Between-hospital variations in depth of coding were present throughout the observation period. Specific comorbidity increases were observed in 15 of the 31 diagnosis groups, and decreases in comorbidity were observed for 11 groups. In patients hospitalized for heart failure, shifts of comorbidity-related risk of in-hospital death occurred in nine diagnosis groups, in which eight groups were directed toward the null. CONCLUSIONS: Comorbidity-adjusted outcomes in longitudinal administrative data analyses may be biased by nonconstant risk over time, changes in completeness of coding, and between-hospital variations in coding. Accounting for such issues is important when the respective observation period coincides with changes in the reimbursement system or other conditions that are likely to alter clinical coding practice.